Overview

Dataset statistics

Number of variables31
Number of observations14942
Missing cells1443
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory248.0 B

Variable types

Numeric11
Categorical15
DateTime5

Alerts

DanceActivities has constant value ""Constant
NatureActivities has constant value ""Constant
Age is highly overall correlated with Income and 2 other fieldsHigh correlation
Income is highly overall correlated with AgeHigh correlation
DaysWithoutFrequency is highly overall correlated with RealNumberOfVisitsHigh correlation
LifetimeValue is highly overall correlated with NumberOfFrequencies and 1 other fieldsHigh correlation
NumberOfFrequencies is highly overall correlated with LifetimeValue and 1 other fieldsHigh correlation
AttendedClasses is highly overall correlated with AllowedWeeklyVisitsBySLA and 1 other fieldsHigh correlation
AllowedWeeklyVisitsBySLA is highly overall correlated with AttendedClasses and 3 other fieldsHigh correlation
AllowedNumberOfVisitsBySLA is highly overall correlated with AttendedClasses and 1 other fieldsHigh correlation
RealNumberOfVisits is highly overall correlated with DaysWithoutFrequencyHigh correlation
NumberOfRenewals is highly overall correlated with LifetimeValue and 1 other fieldsHigh correlation
WaterActivities is highly overall correlated with Age and 2 other fieldsHigh correlation
FitnessActivities is highly overall correlated with Age and 2 other fieldsHigh correlation
HasReferences is highly overall correlated with NumberOfReferencesHigh correlation
NumberOfReferences is highly overall correlated with HasReferencesHigh correlation
UseByTime is highly imbalanced (72.6%)Imbalance
AthleticsActivities is highly imbalanced (93.7%)Imbalance
TeamActivities is highly imbalanced (69.1%)Imbalance
RacketActivities is highly imbalanced (84.0%)Imbalance
CombatActivities is highly imbalanced (50.6%)Imbalance
SpecialActivities is highly imbalanced (82.3%)Imbalance
OtherActivities is highly imbalanced (98.0%)Imbalance
HasReferences is highly imbalanced (85.9%)Imbalance
NumberOfReferences is highly imbalanced (92.4%)Imbalance
Income has 495 (3.3%) missing valuesMissing
AllowedWeeklyVisitsBySLA has 535 (3.6%) missing valuesMissing
ID is uniformly distributedUniform
ID has unique valuesUnique
Income has 2123 (14.2%) zerosZeros
DaysWithoutFrequency has 604 (4.0%) zerosZeros
AttendedClasses has 10432 (69.8%) zerosZeros
RealNumberOfVisits has 2698 (18.1%) zerosZeros
NumberOfRenewals has 6103 (40.8%) zerosZeros

Reproduction

Analysis started2023-11-02 11:25:02.044985
Analysis finished2023-11-02 11:26:21.629493
Duration1 minute and 19.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct14942
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17470.5
Minimum10000
Maximum24941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.9 KiB
2023-11-02T11:26:22.026284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile10747.05
Q113735.25
median17470.5
Q321205.75
95-th percentile24193.95
Maximum24941
Range14941
Interquartile range (IQR)7470.5

Descriptive statistics

Standard deviation4313.5282
Coefficient of variation (CV)0.24690353
Kurtosis-1.2
Mean17470.5
Median Absolute Deviation (MAD)3735.5
Skewness0
Sum2.6104421 × 108
Variance18606526
MonotonicityStrictly increasing
2023-11-02T11:26:22.892773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 1
 
< 0.1%
19966 1
 
< 0.1%
19954 1
 
< 0.1%
19955 1
 
< 0.1%
19956 1
 
< 0.1%
19957 1
 
< 0.1%
19958 1
 
< 0.1%
19959 1
 
< 0.1%
19960 1
 
< 0.1%
19961 1
 
< 0.1%
Other values (14932) 14932
99.9%
ValueCountFrequency (%)
10000 1
< 0.1%
10001 1
< 0.1%
10002 1
< 0.1%
10003 1
< 0.1%
10004 1
< 0.1%
10005 1
< 0.1%
10006 1
< 0.1%
10007 1
< 0.1%
10008 1
< 0.1%
10009 1
< 0.1%
ValueCountFrequency (%)
24941 1
< 0.1%
24940 1
< 0.1%
24939 1
< 0.1%
24938 1
< 0.1%
24937 1
< 0.1%
24936 1
< 0.1%
24935 1
< 0.1%
24934 1
< 0.1%
24933 1
< 0.1%
24932 1
< 0.1%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.015794
Minimum0
Maximum87
Zeros19
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size116.9 KiB
2023-11-02T11:26:23.713911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q119
median23
Q331
95-th percentile56
Maximum87
Range87
Interquartile range (IQR)12

Descriptive statistics

Standard deviation14.156582
Coefficient of variation (CV)0.54415338
Kurtosis1.6773916
Mean26.015794
Median Absolute Deviation (MAD)5
Skewness1.0733012
Sum388728
Variance200.40883
MonotonicityNot monotonic
2023-11-02T11:26:24.438223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 1187
 
7.9%
21 1166
 
7.8%
20 987
 
6.6%
23 954
 
6.4%
19 899
 
6.0%
24 750
 
5.0%
25 583
 
3.9%
26 443
 
3.0%
27 359
 
2.4%
29 297
 
2.0%
Other values (78) 7317
49.0%
ValueCountFrequency (%)
0 19
 
0.1%
1 152
1.0%
2 213
1.4%
3 189
1.3%
4 174
1.2%
5 180
1.2%
6 163
1.1%
7 170
1.1%
8 145
1.0%
9 148
1.0%
ValueCountFrequency (%)
87 1
 
< 0.1%
86 1
 
< 0.1%
85 3
 
< 0.1%
84 2
 
< 0.1%
83 7
< 0.1%
82 5
< 0.1%
81 5
< 0.1%
80 12
0.1%
79 4
 
< 0.1%
78 6
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.9 KiB
Female
8931 
Male
6011 

Length

Max length6
Median length6
Mean length5.1954223
Min length4

Characters and Unicode

Total characters77630
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 8931
59.8%
Male 6011
40.2%

Length

2023-11-02T11:26:25.264522image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-02T11:26:25.979280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
female 8931
59.8%
male 6011
40.2%

Most occurring characters

ValueCountFrequency (%)
e 23873
30.8%
a 14942
19.2%
l 14942
19.2%
F 8931
 
11.5%
m 8931
 
11.5%
M 6011
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 62688
80.8%
Uppercase Letter 14942
 
19.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 23873
38.1%
a 14942
23.8%
l 14942
23.8%
m 8931
 
14.2%
Uppercase Letter
ValueCountFrequency (%)
F 8931
59.8%
M 6011
40.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 77630
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 23873
30.8%
a 14942
19.2%
l 14942
19.2%
F 8931
 
11.5%
m 8931
 
11.5%
M 6011
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 23873
30.8%
a 14942
19.2%
l 14942
19.2%
F 8931
 
11.5%
m 8931
 
11.5%
M 6011
 
7.7%

Income
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct737
Distinct (%)5.1%
Missing495
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean2230.8161
Minimum0
Maximum10890
Zeros2123
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size116.9 KiB
2023-11-02T11:26:26.821481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11470
median1990
Q32790
95-th percentile5400
Maximum10890
Range10890
Interquartile range (IQR)1320

Descriptive statistics

Standard deviation1566.5277
Coefficient of variation (CV)0.70222182
Kurtosis1.4940448
Mean2230.8161
Median Absolute Deviation (MAD)630
Skewness0.9781665
Sum32228600
Variance2454009.1
MonotonicityNot monotonic
2023-11-02T11:26:27.685993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2123
 
14.2%
1890 84
 
0.6%
1820 82
 
0.5%
1970 80
 
0.5%
1710 79
 
0.5%
2160 79
 
0.5%
1730 79
 
0.5%
2030 78
 
0.5%
1700 77
 
0.5%
1510 77
 
0.5%
Other values (727) 11609
77.7%
(Missing) 495
 
3.3%
ValueCountFrequency (%)
0 2123
14.2%
290 1
 
< 0.1%
370 1
 
< 0.1%
390 2
 
< 0.1%
410 1
 
< 0.1%
450 1
 
< 0.1%
470 1
 
< 0.1%
550 1
 
< 0.1%
560 1
 
< 0.1%
620 1
 
< 0.1%
ValueCountFrequency (%)
10890 1
< 0.1%
10260 1
< 0.1%
9840 1
< 0.1%
9720 1
< 0.1%
9630 1
< 0.1%
9600 1
< 0.1%
9530 1
< 0.1%
9400 1
< 0.1%
8980 1
< 0.1%
8950 1
< 0.1%
Distinct1490
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size116.9 KiB
Minimum2014-06-02 00:00:00
Maximum2019-10-21 00:00:00
2023-11-02T11:26:28.526685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:29.397485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1300
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size116.9 KiB
Minimum2014-06-02 00:00:00
Maximum2019-10-31 00:00:00
2023-11-02T11:26:30.249917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:31.168430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size116.9 KiB
Minimum2014-01-01 00:00:00
Maximum2019-07-01 00:00:00
2023-11-02T11:26:32.124265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:32.844443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size116.9 KiB
Minimum2014-12-31 00:00:00
Maximum2019-12-31 00:00:00
2023-11-02T11:26:33.552467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:34.268659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
Distinct1384
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size116.9 KiB
Minimum2014-07-03 00:00:00
Maximum2019-10-31 00:00:00
2023-11-02T11:26:35.204361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:36.464921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DaysWithoutFrequency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct660
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.224936
Minimum0
Maximum1745
Zeros604
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size116.9 KiB
2023-11-02T11:26:37.321200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q113
median41
Q383.75
95-th percentile300
Maximum1745
Range1745
Interquartile range (IQR)70.75

Descriptive statistics

Standard deviation144.19958
Coefficient of variation (CV)1.7753116
Kurtosis36.668263
Mean81.224936
Median Absolute Deviation (MAD)30
Skewness5.1947271
Sum1213663
Variance20793.518
MonotonicityNot monotonic
2023-11-02T11:26:38.464288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 604
 
4.0%
1 563
 
3.8%
5 393
 
2.6%
2 380
 
2.5%
3 349
 
2.3%
12 282
 
1.9%
13 220
 
1.5%
6 220
 
1.5%
39 207
 
1.4%
41 196
 
1.3%
Other values (650) 11528
77.2%
ValueCountFrequency (%)
0 604
4.0%
1 563
3.8%
2 380
2.5%
3 349
2.3%
4 99
 
0.7%
5 393
2.6%
6 220
 
1.5%
7 137
 
0.9%
8 176
 
1.2%
9 142
 
1.0%
ValueCountFrequency (%)
1745 1
< 0.1%
1717 1
< 0.1%
1710 1
< 0.1%
1681 1
< 0.1%
1670 1
< 0.1%
1633 1
< 0.1%
1630 1
< 0.1%
1624 1
< 0.1%
1620 1
< 0.1%
1603 1
< 0.1%

LifetimeValue
Real number (ℝ)

HIGH CORRELATION 

Distinct5668
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean302.56187
Minimum0
Maximum6727.8
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size116.9 KiB
2023-11-02T11:26:39.278543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.6
Q183.6
median166.2
Q3355.075
95-th percentile1072.3135
Maximum6727.8
Range6727.8
Interquartile range (IQR)271.475

Descriptive statistics

Standard deviation364.31957
Coefficient of variation (CV)1.2041159
Kurtosis18.266694
Mean302.56187
Median Absolute Deviation (MAD)101
Skewness3.0542837
Sum4520879.5
Variance132728.75
MonotonicityNot monotonic
2023-11-02T11:26:39.944356image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.6 188
 
1.3%
53.6 146
 
1.0%
47.2 129
 
0.9%
37.6 124
 
0.8%
83.6 118
 
0.8%
73.6 114
 
0.8%
61.6 100
 
0.7%
38.5 73
 
0.5%
103.6 68
 
0.5%
113.6 67
 
0.4%
Other values (5658) 13815
92.5%
ValueCountFrequency (%)
0 3
< 0.1%
1.3 1
 
< 0.1%
3.6 2
< 0.1%
5.4 4
< 0.1%
10.6 1
 
< 0.1%
15.7 1
 
< 0.1%
18.4 1
 
< 0.1%
20 1
 
< 0.1%
20.9 2
< 0.1%
21 1
 
< 0.1%
ValueCountFrequency (%)
6727.8 1
< 0.1%
6232.2 1
< 0.1%
3498.4 1
< 0.1%
3145.85 1
< 0.1%
3137.35 1
< 0.1%
2938.8 1
< 0.1%
2864.72 1
< 0.1%
2676.3 1
< 0.1%
2636.77 1
< 0.1%
2590.15 1
< 0.1%

UseByTime
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.9 KiB
0
14238 
1
 
704

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14942
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14238
95.3%
1 704
 
4.7%

Length

2023-11-02T11:26:41.014756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-02T11:26:41.957038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 14238
95.3%
1 704
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 14238
95.3%
1 704
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14942
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14238
95.3%
1 704
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 14942
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14238
95.3%
1 704
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14942
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14238
95.3%
1 704
 
4.7%

AthleticsActivities
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing36
Missing (%)0.2%
Memory size116.9 KiB
0.0
14796 
1.0
 
110

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters44718
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14796
99.0%
1.0 110
 
0.7%
(Missing) 36
 
0.2%

Length

2023-11-02T11:26:42.578810image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-02T11:26:43.417152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14796
99.3%
1.0 110
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 29702
66.4%
. 14906
33.3%
1 110
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29812
66.7%
Other Punctuation 14906
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29702
99.6%
1 110
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 14906
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44718
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29702
66.4%
. 14906
33.3%
1 110
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29702
66.4%
. 14906
33.3%
1 110
 
0.2%

WaterActivities
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing37
Missing (%)0.2%
Memory size116.9 KiB
0.0
10490 
1.0
4415 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters44715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 10490
70.2%
1.0 4415
29.5%
(Missing) 37
 
0.2%

Length

2023-11-02T11:26:44.030048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-02T11:26:44.454563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 10490
70.4%
1.0 4415
29.6%

Most occurring characters

ValueCountFrequency (%)
0 25395
56.8%
. 14905
33.3%
1 4415
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29810
66.7%
Other Punctuation 14905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25395
85.2%
1 4415
 
14.8%
Other Punctuation
ValueCountFrequency (%)
. 14905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25395
56.8%
. 14905
33.3%
1 4415
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25395
56.8%
. 14905
33.3%
1 4415
 
9.9%

FitnessActivities
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing35
Missing (%)0.2%
Memory size116.9 KiB
1.0
8587 
0.0
6320 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters44721
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 8587
57.5%
0.0 6320
42.3%
(Missing) 35
 
0.2%

Length

2023-11-02T11:26:44.839529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-02T11:26:45.224751image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8587
57.6%
0.0 6320
42.4%

Most occurring characters

ValueCountFrequency (%)
0 21227
47.5%
. 14907
33.3%
1 8587
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29814
66.7%
Other Punctuation 14907
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21227
71.2%
1 8587
28.8%
Other Punctuation
ValueCountFrequency (%)
. 14907
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44721
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21227
47.5%
. 14907
33.3%
1 8587
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21227
47.5%
. 14907
33.3%
1 8587
19.2%

DanceActivities
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing36
Missing (%)0.2%
Memory size116.9 KiB
0.0
14906 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters44718
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14906
99.8%
(Missing) 36
 
0.2%

Length

2023-11-02T11:26:46.281071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-02T11:26:46.864950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14906
100.0%

Most occurring characters

ValueCountFrequency (%)
0 29812
66.7%
. 14906
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29812
66.7%
Other Punctuation 14906
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29812
100.0%
Other Punctuation
ValueCountFrequency (%)
. 14906
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44718
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29812
66.7%
. 14906
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29812
66.7%
. 14906
33.3%

TeamActivities
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing35
Missing (%)0.2%
Memory size116.9 KiB
0.0
14079 
1.0
 
828

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters44721
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14079
94.2%
1.0 828
 
5.5%
(Missing) 35
 
0.2%

Length

2023-11-02T11:26:47.204439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-02T11:26:47.617109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14079
94.4%
1.0 828
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 28986
64.8%
. 14907
33.3%
1 828
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29814
66.7%
Other Punctuation 14907
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28986
97.2%
1 828
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 14907
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44721
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28986
64.8%
. 14907
33.3%
1 828
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28986
64.8%
. 14907
33.3%
1 828
 
1.9%

RacketActivities
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing37
Missing (%)0.2%
Memory size116.9 KiB
0.0
14556 
1.0
 
349

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters44715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14556
97.4%
1.0 349
 
2.3%
(Missing) 37
 
0.2%

Length

2023-11-02T11:26:48.469176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-02T11:26:49.665691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14556
97.7%
1.0 349
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 29461
65.9%
. 14905
33.3%
1 349
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29810
66.7%
Other Punctuation 14905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29461
98.8%
1 349
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 14905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29461
65.9%
. 14905
33.3%
1 349
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29461
65.9%
. 14905
33.3%
1 349
 
0.8%

CombatActivities
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing33
Missing (%)0.2%
Memory size116.9 KiB
0.0
13300 
1.0
1609 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters44727
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 13300
89.0%
1.0 1609
 
10.8%
(Missing) 33
 
0.2%

Length

2023-11-02T11:26:50.297226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-02T11:26:50.657173image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 13300
89.2%
1.0 1609
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 28209
63.1%
. 14909
33.3%
1 1609
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29818
66.7%
Other Punctuation 14909
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28209
94.6%
1 1609
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 14909
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44727
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28209
63.1%
. 14909
33.3%
1 1609
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28209
63.1%
. 14909
33.3%
1 1609
 
3.6%

NatureActivities
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing47
Missing (%)0.3%
Memory size116.9 KiB
0.0
14895 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters44685
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14895
99.7%
(Missing) 47
 
0.3%

Length

2023-11-02T11:26:51.116041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-02T11:26:51.594605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14895
100.0%

Most occurring characters

ValueCountFrequency (%)
0 29790
66.7%
. 14895
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29790
66.7%
Other Punctuation 14895
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29790
100.0%
Other Punctuation
ValueCountFrequency (%)
. 14895
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44685
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29790
66.7%
. 14895
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44685
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29790
66.7%
. 14895
33.3%

SpecialActivities
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing44
Missing (%)0.3%
Memory size116.9 KiB
0.0
14503 
1.0
 
395

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters44694
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14503
97.1%
1.0 395
 
2.6%
(Missing) 44
 
0.3%

Length

2023-11-02T11:26:51.874623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-02T11:26:52.089176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14503
97.3%
1.0 395
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 29401
65.8%
. 14898
33.3%
1 395
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29796
66.7%
Other Punctuation 14898
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29401
98.7%
1 395
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 14898
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44694
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29401
65.8%
. 14898
33.3%
1 395
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29401
65.8%
. 14898
33.3%
1 395
 
0.9%

OtherActivities
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing35
Missing (%)0.2%
Memory size116.9 KiB
0.0
14879 
1.0
 
28

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters44721
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14879
99.6%
1.0 28
 
0.2%
(Missing) 35
 
0.2%

Length

2023-11-02T11:26:52.510509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-02T11:26:53.377988image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14879
99.8%
1.0 28
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 29786
66.6%
. 14907
33.3%
1 28
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29814
66.7%
Other Punctuation 14907
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29786
99.9%
1 28
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 14907
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44721
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29786
66.6%
. 14907
33.3%
1 28
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29786
66.6%
. 14907
33.3%
1 28
 
0.1%

NumberOfFrequencies
Real number (ℝ)

HIGH CORRELATION 

Distinct415
Distinct (%)2.8%
Missing26
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean40.120542
Minimum1
Maximum1031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.9 KiB
2023-11-02T11:26:54.329606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median18
Q345
95-th percentile152
Maximum1031
Range1030
Interquartile range (IQR)38

Descriptive statistics

Standard deviation65.466459
Coefficient of variation (CV)1.6317441
Kurtosis37.729619
Mean40.120542
Median Absolute Deviation (MAD)14
Skewness4.8438354
Sum598438
Variance4285.8573
MonotonicityNot monotonic
2023-11-02T11:26:55.597636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 661
 
4.4%
3 623
 
4.2%
1 609
 
4.1%
4 583
 
3.9%
5 556
 
3.7%
6 521
 
3.5%
7 462
 
3.1%
8 439
 
2.9%
9 417
 
2.8%
10 383
 
2.6%
Other values (405) 9662
64.7%
ValueCountFrequency (%)
1 609
4.1%
2 661
4.4%
3 623
4.2%
4 583
3.9%
5 556
3.7%
6 521
3.5%
7 462
3.1%
8 439
2.9%
9 417
2.8%
10 383
2.6%
ValueCountFrequency (%)
1031 1
< 0.1%
961 1
< 0.1%
954 1
< 0.1%
893 2
< 0.1%
888 1
< 0.1%
824 1
< 0.1%
769 1
< 0.1%
768 1
< 0.1%
735 1
< 0.1%
727 1
< 0.1%

AttendedClasses
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct230
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.152456
Minimum0
Maximum581
Zeros10432
Zeros (%)69.8%
Negative0
Negative (%)0.0%
Memory size116.9 KiB
2023-11-02T11:26:56.467153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile63
Maximum581
Range581
Interquartile range (IQR)3

Descriptive statistics

Standard deviation29.154202
Coefficient of variation (CV)2.8716403
Kurtosis43.683013
Mean10.152456
Median Absolute Deviation (MAD)0
Skewness5.2509804
Sum151698
Variance849.96752
MonotonicityNot monotonic
2023-11-02T11:26:57.393515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10432
69.8%
1 411
 
2.8%
2 214
 
1.4%
3 195
 
1.3%
4 171
 
1.1%
6 150
 
1.0%
5 144
 
1.0%
7 139
 
0.9%
9 113
 
0.8%
10 112
 
0.7%
Other values (220) 2861
 
19.1%
ValueCountFrequency (%)
0 10432
69.8%
1 411
 
2.8%
2 214
 
1.4%
3 195
 
1.3%
4 171
 
1.1%
5 144
 
1.0%
6 150
 
1.0%
7 139
 
0.9%
8 96
 
0.6%
9 113
 
0.8%
ValueCountFrequency (%)
581 1
< 0.1%
547 1
< 0.1%
406 1
< 0.1%
355 1
< 0.1%
334 1
< 0.1%
324 1
< 0.1%
318 2
< 0.1%
312 1
< 0.1%
300 1
< 0.1%
297 1
< 0.1%

AllowedWeeklyVisitsBySLA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing535
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean5.7595613
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.9 KiB
2023-11-02T11:26:58.251602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median7
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1188667
Coefficient of variation (CV)0.36788682
Kurtosis-0.31894013
Mean5.7595613
Median Absolute Deviation (MAD)0
Skewness-1.2287199
Sum82978
Variance4.489596
MonotonicityNot monotonic
2023-11-02T11:26:59.111742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 10505
70.3%
2 2341
 
15.7%
4 742
 
5.0%
1 525
 
3.5%
3 160
 
1.1%
6 118
 
0.8%
5 16
 
0.1%
(Missing) 535
 
3.6%
ValueCountFrequency (%)
1 525
 
3.5%
2 2341
 
15.7%
3 160
 
1.1%
4 742
 
5.0%
5 16
 
0.1%
6 118
 
0.8%
7 10505
70.3%
ValueCountFrequency (%)
7 10505
70.3%
6 118
 
0.8%
5 16
 
0.1%
4 742
 
5.0%
3 160
 
1.1%
2 2341
 
15.7%
1 525
 
3.5%

AllowedNumberOfVisitsBySLA
Real number (ℝ)

HIGH CORRELATION 

Distinct270
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.636299
Minimum0.56
Maximum240.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.9 KiB
2023-11-02T11:26:59.717446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.56
5-th percentile8.71
Q125.72
median38.99
Q360.97
95-th percentile62.02
Maximum240.03
Range239.47
Interquartile range (IQR)35.25

Descriptive statistics

Standard deviation21.066166
Coefficient of variation (CV)0.50595673
Kurtosis9.7532715
Mean41.636299
Median Absolute Deviation (MAD)21.57
Skewness1.0902016
Sum622129.58
Variance443.78334
MonotonicityNot monotonic
2023-11-02T11:27:00.461118image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.97 4462
29.9%
17.42 1497
 
10.0%
30.03 1205
 
8.1%
28.98 1138
 
7.6%
62.02 646
 
4.3%
59.01 508
 
3.4%
34.84 483
 
3.2%
8.71 367
 
2.5%
21.98 286
 
1.9%
67.97 285
 
1.9%
Other values (260) 4065
27.2%
ValueCountFrequency (%)
0.56 2
< 0.1%
0.71 4
< 0.1%
0.86 1
 
< 0.1%
1.42 1
 
< 0.1%
1.72 4
< 0.1%
1.86 2
< 0.1%
2 1
 
< 0.1%
2.03 2
< 0.1%
2.28 2
< 0.1%
2.29 2
< 0.1%
ValueCountFrequency (%)
240.03 20
0.1%
224 1
 
< 0.1%
206.01 1
 
< 0.1%
178.01 2
 
< 0.1%
156.03 1
 
< 0.1%
144.97 1
 
< 0.1%
137.16 4
 
< 0.1%
104.13 2
 
< 0.1%
103.71 1
 
< 0.1%
95.97 2
 
< 0.1%

RealNumberOfVisits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3207067
Minimum0
Maximum84
Zeros2698
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size116.9 KiB
2023-11-02T11:27:01.529655image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q37
95-th percentile17
Maximum84
Range84
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.3329585
Coefficient of variation (CV)1.1902476
Kurtosis14.621097
Mean5.3207067
Median Absolute Deviation (MAD)3
Skewness2.8727388
Sum79502
Variance40.106363
MonotonicityNot monotonic
2023-11-02T11:27:02.254785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2698
18.1%
1 1641
11.0%
2 1635
10.9%
3 1440
9.6%
4 1323
8.9%
5 1045
 
7.0%
6 913
 
6.1%
7 711
 
4.8%
8 611
 
4.1%
9 466
 
3.1%
Other values (50) 2459
16.5%
ValueCountFrequency (%)
0 2698
18.1%
1 1641
11.0%
2 1635
10.9%
3 1440
9.6%
4 1323
8.9%
5 1045
 
7.0%
6 913
 
6.1%
7 711
 
4.8%
8 611
 
4.1%
9 466
 
3.1%
ValueCountFrequency (%)
84 2
< 0.1%
72 3
< 0.1%
66 2
< 0.1%
58 1
 
< 0.1%
57 1
 
< 0.1%
56 1
 
< 0.1%
53 1
 
< 0.1%
52 1
 
< 0.1%
51 2
< 0.1%
50 1
 
< 0.1%

NumberOfRenewals
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2052603
Minimum0
Maximum6
Zeros6103
Zeros (%)40.8%
Negative0
Negative (%)0.0%
Memory size116.9 KiB
2023-11-02T11:27:03.197737image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3813047
Coefficient of variation (CV)1.1460633
Kurtosis0.83610407
Mean1.2052603
Median Absolute Deviation (MAD)1
Skewness1.198546
Sum18009
Variance1.9080026
MonotonicityNot monotonic
2023-11-02T11:27:03.928957image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 6103
40.8%
1 4020
26.9%
2 2347
 
15.7%
3 1209
 
8.1%
4 733
 
4.9%
5 444
 
3.0%
6 86
 
0.6%
ValueCountFrequency (%)
0 6103
40.8%
1 4020
26.9%
2 2347
 
15.7%
3 1209
 
8.1%
4 733
 
4.9%
5 444
 
3.0%
6 86
 
0.6%
ValueCountFrequency (%)
6 86
 
0.6%
5 444
 
3.0%
4 733
 
4.9%
3 1209
 
8.1%
2 2347
 
15.7%
1 4020
26.9%
0 6103
40.8%

HasReferences
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing12
Missing (%)0.1%
Memory size116.9 KiB
0.0
14633 
1.0
 
297

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters44790
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14633
97.9%
1.0 297
 
2.0%
(Missing) 12
 
0.1%

Length

2023-11-02T11:27:04.631365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-02T11:27:05.177483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14633
98.0%
1.0 297
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 29563
66.0%
. 14930
33.3%
1 297
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29860
66.7%
Other Punctuation 14930
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29563
99.0%
1 297
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 14930
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44790
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29563
66.0%
. 14930
33.3%
1 297
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44790
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29563
66.0%
. 14930
33.3%
1 297
 
0.7%

NumberOfReferences
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.9 KiB
0
14646 
1
 
267
2
 
21
3
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14942
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14646
98.0%
1 267
 
1.8%
2 21
 
0.1%
3 8
 
0.1%

Length

2023-11-02T11:27:06.003363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-02T11:27:06.461048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 14646
98.0%
1 267
 
1.8%
2 21
 
0.1%
3 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 14646
98.0%
1 267
 
1.8%
2 21
 
0.1%
3 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14942
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14646
98.0%
1 267
 
1.8%
2 21
 
0.1%
3 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14942
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14646
98.0%
1 267
 
1.8%
2 21
 
0.1%
3 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14942
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14646
98.0%
1 267
 
1.8%
2 21
 
0.1%
3 8
 
0.1%

Dropout
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.9 KiB
1
11968 
0
2974 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14942
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 11968
80.1%
0 2974
 
19.9%

Length

2023-11-02T11:27:06.867517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-02T11:27:07.424645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 11968
80.1%
0 2974
 
19.9%

Most occurring characters

ValueCountFrequency (%)
1 11968
80.1%
0 2974
 
19.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14942
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11968
80.1%
0 2974
 
19.9%

Most occurring scripts

ValueCountFrequency (%)
Common 14942
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11968
80.1%
0 2974
 
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14942
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11968
80.1%
0 2974
 
19.9%

Interactions

2023-11-02T11:26:09.964365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:09.540141image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:15.177917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:20.507158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:27.771080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:33.441375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:37.658230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:44.899066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:51.374653image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:57.138499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:04.184726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:10.527584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:10.009099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:15.757076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:22.242020image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:28.254446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:33.791853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:38.254335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:45.516814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:51.962299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:57.804773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:04.804673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:11.060263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:10.766722image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:16.197672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:22.635293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:28.754875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:34.096738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:38.740428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:46.015202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:52.439571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:58.371147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:05.234308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:11.575452image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:11.467892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:16.504755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:23.034593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:29.244776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:34.399548image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:39.354415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:46.554799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:52.847020image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:59.080389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:05.737256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:12.326045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:11.994755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:16.877427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:23.673896image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:29.794741image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:34.784122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:40.318443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:47.176180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:53.286509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:59.805784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:06.249671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:12.984570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:12.314438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:17.464677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:24.439574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:30.505499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:35.105875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:40.984600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:47.714571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:53.938363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:00.519834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:07.217536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:13.546671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:12.647096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:18.051151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:25.044361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:31.119128image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:35.430669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:41.604749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:48.479843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:54.424472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:01.138327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:07.671214image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:14.049490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:12.997804image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:18.577078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:25.604719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:31.593256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:35.769012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:42.354844image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:49.050356image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:55.004363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:01.644983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:08.187989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:14.647488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:13.444671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:19.179504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:26.186839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:32.244519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:36.101004image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:43.044700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:49.706633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:55.564439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:02.256878image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:08.699562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:15.179984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:13.945265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:19.937355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:26.718263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:32.659498image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:36.618015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:43.677555image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:50.229370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:56.024893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:02.864727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:09.072637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:15.795800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:14.606791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:20.232748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:27.209428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:33.036938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:37.124480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:44.317620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:50.744133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:25:56.634587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:03.584578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-02T11:26:09.466006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-02T11:27:07.740456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
IDAgeIncomeDaysWithoutFrequencyLifetimeValueNumberOfFrequenciesAttendedClassesAllowedWeeklyVisitsBySLAAllowedNumberOfVisitsBySLARealNumberOfVisitsNumberOfRenewalsGenderUseByTimeAthleticsActivitiesWaterActivitiesFitnessActivitiesTeamActivitiesRacketActivitiesCombatActivitiesSpecialActivitiesOtherActivitiesHasReferencesNumberOfReferencesDropout
ID1.000-0.015-0.010-0.006-0.001-0.0040.007-0.011-0.0030.009-0.0100.0070.0000.0000.0000.0000.0000.0200.0000.0000.0160.0240.0120.000
Age-0.0151.0000.8650.023-0.0220.085-0.3070.3740.3060.0740.0000.0660.1070.0460.5260.5400.3230.0860.1220.2580.0750.1990.1200.290
Income-0.0100.8651.0000.011-0.0320.059-0.2740.3360.2610.067-0.0070.0420.0970.0250.4370.4600.1690.0200.0670.1970.0540.1880.1130.232
DaysWithoutFrequency-0.0060.0230.0111.000-0.117-0.272-0.1020.0210.103-0.5390.0720.0370.1300.0310.0420.0290.0600.0510.0190.0240.0000.0360.0200.092
LifetimeValue-0.001-0.022-0.032-0.1171.0000.7440.491-0.381-0.0880.0110.6650.0650.0200.0000.3120.2240.0770.0000.0000.0860.0000.2040.1220.310
NumberOfFrequencies-0.0040.0850.059-0.2720.7441.0000.244-0.0580.0810.4120.5440.0660.0950.0530.1100.0120.0000.0220.0310.1880.0000.0900.0510.218
AttendedClasses0.007-0.307-0.274-0.1020.4910.2441.000-0.846-0.556-0.0730.3080.0000.0340.0000.2930.2260.0430.0000.0620.0360.0000.1860.1130.248
AllowedWeeklyVisitsBySLA-0.0110.3740.3360.021-0.381-0.058-0.8461.0000.6930.166-0.2010.0000.1140.0160.5300.6000.4000.2720.1700.0310.1340.1660.1030.221
AllowedNumberOfVisitsBySLA-0.0030.3060.2610.103-0.0880.081-0.5560.6931.0000.070-0.1310.0360.0770.0190.4400.4790.1490.1940.1860.1010.3280.1240.0740.286
RealNumberOfVisits0.0090.0740.067-0.5390.0110.412-0.0730.1660.0701.0000.0220.0690.0690.0580.0300.1160.0690.0200.0820.1100.0000.0220.0000.161
NumberOfRenewals-0.0100.000-0.0070.0720.6650.5440.308-0.201-0.1310.0221.0000.0780.1280.0190.2240.1420.1000.0260.0000.1090.0520.1850.1130.305
Gender0.0070.0660.0420.0370.0650.0660.0000.0000.0360.0690.0781.0000.0390.0210.0000.1040.0950.0190.1160.0290.0330.0000.0000.029
UseByTime0.0000.1070.0970.1300.0200.0950.0340.1140.0770.0690.1280.0391.0000.0090.0000.0640.0300.0190.0150.0110.0000.0060.0000.110
AthleticsActivities0.0000.0460.0250.0310.0000.0530.0000.0160.0190.0580.0190.0210.0091.0000.0260.0690.0000.0070.0140.0090.0000.0100.0120.007
WaterActivities0.0000.5260.4370.0420.3120.1100.2930.5300.4400.0300.2240.0000.0000.0261.0000.6190.0730.0790.1640.0450.0210.1370.1430.184
FitnessActivities0.0000.5400.4600.0290.2240.0120.2260.6000.4790.1160.1420.1040.0640.0690.6191.0000.2440.1460.2530.0580.0420.1080.1140.154
TeamActivities0.0000.3230.1690.0600.0770.0000.0430.4000.1490.0690.1000.0950.0300.0000.0730.2441.0000.0080.0470.0250.0000.0280.0290.062
RacketActivities0.0200.0860.0200.0510.0000.0220.0000.2720.1940.0200.0260.0190.0190.0070.0790.1460.0081.0000.0350.0140.0000.0160.0230.063
CombatActivities0.0000.1220.0670.0190.0000.0310.0620.1700.1860.0820.0000.1160.0150.0140.1640.2530.0470.0351.0000.0380.0100.0060.0090.000
SpecialActivities0.0000.2580.1970.0240.0860.1880.0360.0310.1010.1100.1090.0290.0110.0090.0450.0580.0250.0140.0381.0000.1060.0140.0100.056
OtherActivities0.0160.0750.0540.0000.0000.0000.0000.1340.3280.0000.0520.0330.0000.0000.0210.0420.0000.0000.0100.1061.0000.0000.0000.018
HasReferences0.0240.1990.1880.0360.2040.0900.1860.1660.1240.0220.1850.0000.0060.0100.1370.1080.0280.0160.0060.0140.0001.0000.9770.018
NumberOfReferences0.0120.1200.1130.0200.1220.0510.1130.1030.0740.0000.1130.0000.0000.0120.1430.1140.0290.0230.0090.0100.0000.9771.0000.013
Dropout0.0000.2900.2320.0920.3100.2180.2480.2210.2860.1610.3050.0290.1100.0070.1840.1540.0620.0630.0000.0560.0180.0180.0131.000

Missing values

2023-11-02T11:26:16.809868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-02T11:26:18.974981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-02T11:26:20.824900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDAgeGenderIncomeEnrollmentStartEnrollmentFinishLastPeriodStartLastPeriodFinishDateLastVisitDaysWithoutFrequencyLifetimeValueUseByTimeAthleticsActivitiesWaterActivitiesFitnessActivitiesDanceActivitiesTeamActivitiesRacketActivitiesCombatActivitiesNatureActivitiesSpecialActivitiesOtherActivitiesNumberOfFrequenciesAttendedClassesAllowedWeeklyVisitsBySLAAllowedNumberOfVisitsBySLARealNumberOfVisitsNumberOfRenewalsHasReferencesNumberOfReferencesDropout
01000060Female5500.02019-09-032019-10-312019-07-012019-12-312019-10-30189.3500.00.01.00.00.00.00.00.00.00.09.07NaN6.28200.000
11000129Female2630.02014-08-122015-09-142015-01-012015-12-312015-07-1660479.2000.00.00.00.00.00.00.00.01.00.023.012.017.42120.001
21000223Male1980.02017-05-022017-06-012017-01-012017-06-302017-05-25737.6000.00.01.00.00.00.00.00.00.00.06.007.030.03600.001
3100039Male0.02018-09-052019-02-122018-07-012019-06-302019-01-2122155.4000.00.00.00.01.00.00.00.00.00.020.022.017.72300.001
41000435Male4320.02016-04-202018-06-072018-01-012018-06-302017-11-09210373.2000.00.01.00.00.00.00.00.00.0NaN41.007.060.97030.001
51000524Female2220.02015-12-012016-07-312016-01-012016-12-312016-07-283140.0000.00.01.00.00.00.00.00.00.00.028.007.059.012600.001
61000623Male2340.02015-10-062019-04-112019-01-012019-06-302019-03-2616143.5000.00.01.00.00.00.00.00.00.00.022.007.030.03330.001
71000723Male1910.02015-03-252019-07-312019-01-012019-06-302019-04-2399233.9010.00.01.00.00.00.00.00.00.00.034.007.060.971150.001
81000829Male3220.02016-04-082017-06-072017-01-012017-06-302017-03-3069212.6000.00.00.00.00.00.01.00.00.00.06.007.060.97020.001
91000914Female0.02016-07-072017-02-012016-07-012017-06-302016-11-1974253.5000.00.00.00.01.00.00.00.00.00.06.062.017.72020.001
IDAgeGenderIncomeEnrollmentStartEnrollmentFinishLastPeriodStartLastPeriodFinishDateLastVisitDaysWithoutFrequencyLifetimeValueUseByTimeAthleticsActivitiesWaterActivitiesFitnessActivitiesDanceActivitiesTeamActivitiesRacketActivitiesCombatActivitiesNatureActivitiesSpecialActivitiesOtherActivitiesNumberOfFrequenciesAttendedClassesAllowedWeeklyVisitsBySLAAllowedNumberOfVisitsBySLARealNumberOfVisitsNumberOfRenewalsHasReferencesNumberOfReferencesDropout
149322493220Female1590.02018-02-272018-07-312018-01-012018-12-312018-06-154692.4000.01.00.00.00.00.00.00.00.00.04.007.066.99400.001
149332493318Male1540.02016-10-112016-11-112016-07-012016-12-312016-11-10147.6000.00.01.00.00.00.00.00.00.00.02.007.030.03200.001
149342493421Male2170.02018-04-042018-06-072018-01-012018-06-302018-04-046450.4000.00.01.00.00.00.00.00.00.00.01.007.060.97000.001
149352493523Female1440.02016-11-022018-01-072017-07-012018-06-302017-10-2376248.0000.00.01.00.00.00.00.00.00.00.089.007.060.97010.001
149362493621Female1820.02016-11-112017-08-292017-01-012017-12-312017-06-1476124.5000.00.00.00.00.00.01.00.00.00.012.007.060.97120.001
149372493714Male0.02016-09-082016-09-082019-07-012019-12-312019-10-2921460.4500.01.00.00.00.00.00.00.00.00.0112.0964.034.84830.000
149382493839MaleNaN2015-09-172016-06-042016-01-012016-06-302016-04-2738343.8500.00.00.00.00.00.01.00.00.00.057.007.060.97300.001
149392493920Male1810.02017-03-012017-03-312017-01-012017-06-302017-03-29243.6000.00.01.00.00.00.00.00.00.00.06.007.030.03800.001
149402494055Male4800.02018-03-012018-03-012019-07-012019-12-312019-10-283788.6000.01.00.00.00.00.00.00.00.00.0185.01553.015.87720.000
149412494132Female3700.02016-04-012019-06-112019-01-012019-06-302019-05-2814919.6000.00.01.00.00.00.00.00.00.00.0169.017.060.971340.001